Glaucoma is the second leading cause of blindness in the U.S. It is characterized by loss of retinal ganglion cells, thinning of the retinal nerve fiber layer, or cupping of the optic disc. Conventional tests are based on measurements of intraocular pressure and visual field tests. Because there may be significant structural loss before functional loss begin to manifest, these tests often miss early stages of the disease. In theory, diagnosis based on structural loss should be able to detect glaucoma at a stage earlier than detectable visual field defects. Structural damages associated with glaucoma are characteristically distributed in the above mentioned three anatomic regions, thus, diagnostic tools capable of providing information on structural changes on these anatomic regions are potentially useful for glaucoma diagnosis.
To this end, there are currently a number of diagnostic methods and tools capable of providing direct and indirect information on different anatomic regions of the eye. In particular, the recent introduction of Fourier-domain optical coherence tomography (FD-OCT) enables high density retinal mapping over a large area in a short period of time. The short image acquisition time reduces motion error, and the high image density and large image area permits more detailed pattern analysis. FIG. 1 shows an example of a FD-OCT image depicting the structural loss due to glaucoma in the posterior segment of the eye.
Ironically, these technological advances have created an informational crisis. Given the variety of methods and instrumentations providing a bewilderment of diagnostic information, how to interpret and combine these diverse sources of information to arrive at a meaningful clinical diagnosis has become a major challenge. Moreover, while imaging technologies are now available to provide detailed images of the eye, qualitative interpretation of these images by trained experts can vary widely. Thus, the information has not led to better or easier clinical decision making.
Therefore, there exists an urgent need for automated, quantitative methods of analyzing imaging data to arrive at reliable and reproducible diagnosis of glaucoma.